Jian Zhang

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Q2 Description, Results, and Plans

• Jian Zhang
• Hydrometeorology

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Q2 System Overview Flowchart
Radar
Quality Control
Satellite
3-D Radar Mosaic
QPE
QPF (0-2h)
Rain Gauge
Model
Precip Products
Mosaic Products
Sfc Obs Sounding
Verification
Users
Lightning
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Q2 Components

• Reflectivity quality control (QC) (Lakshmanan et
  al. 2007, JTECH Gourley et al. 2007, JTECH)
• 3-D reflectivity mosaic (Zhang et al. 2005,
  JTECH Langston et al. 2007, JTECH Yang et al.
  2009, AAS)
• Precipitation classification (Xu et al. 2008, J.
  Hydromet Zhang et al. 2008, JTECH)
• Stratiform, Convective, Hail, Tropical Rain, and
  Snow
• Adaptive Z (reflectivity) - R (rainfall rate)
  relationships (Xu et al. 2008, J. Hydromet)
• Seamless hybrid scan reflectivity (HSR) mosaic
• Local gauge bias correction (in preparation)
• Vertical profile of reflectivity (VPR) correction
  for bright band (Zhang et al. 2008, JTECH)
• Non-standard blockage mitigation (Chang et al.
  2009, JTECH)
• Multi-sensor quantitative precipitation
  estimation (QPE) uncertainties

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Automated Reflectivity Quality Control
Objective to remove non-precipitation
echoes. Performance gt95 Remaining challenges
nocturnal AP migrating birds. Future dual-pol
hydrometeor/scatterer classification
Before QC
After QC
Publications Lakshmanan, V., A. Fritz, T. Smith,
K. Hondl, and G. J. Stumpf, 2007 An automated
technique to quality control radar reflectivity
data. J. Appl. Meteor., 46, 288305. Gourley,
J.J., P. Tabary, and J. Parent-du-Chatelet, 2007
A fuzzy logic algorithm for the separation of
precipitating from non-precipitating echoes using
polarimetric radar observations. J. Atmo. and
Ocean. Tech., 24, 1439-1451.
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3-D Reflectivity Mosaic
Objective depict high-resolution 3-D storm
structure Performance transferred to operations
at NCEP and improved short term precipitation
forecast part of the FAAs Weather
Cube Remaining challenges low vertical
resolution Future Phase Array Radar will provide
better vertical resolution
Dallas Hail Storm 5/5/95
Publications Zhang, J., K. Howard, and J.J.
Gourley, 2005 Constructing three-dimensional
multiple radar reflectivity mosaics examples of
convective storms and stratiform rain echoes. J.
Atmos. Ocean. Tech., 22, 30-42. Langston, C., J.
Zhang, and K. Howard, 2007 Four-dimensional
dynamic radar mosaic. J. Atmos. Ocean. Tech., 24,
776-790.
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Radar QPEPrecipitation Classification
Objective To obtain accurate, high-resolution
precipitation estimation.
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Convective/Stratiform/Hail
Need more surface validation data gt Severe
Hazards Analysis Verification Experiment
(SHAVE) project
Radar Reflectivity
Precipitation Classification
Publications Zhang, J., C. Langston, and K.
Howard, 2008 Bright Band Identification Based On
Vertical Profiles of Reflectivity from the
WSR-88D. J. Atmos. Ocean. Tech. 25, 1859-1872.
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Tropical Rain Identification
Typhoon Krosa, 10/6/07 Taiwan
Publications Xu, X., K. Howard, and J. Zhang,
2008 An automated radar technique for the
identification of tropical precipitation. J.
Hydrometeorology. 9, 885-902.
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Rain/Snow Delineation
RUC Surface Analysis
Environmental data is extremely important for
precipitation classification gt true both for
single and dual-pol radar applications!
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Adaptive Reflectivity-Rainfall (Z-R) Relationships
rain rate
convective
Reflectivity
tropical
Taiwan
stratiform
snow
Pcp Type
1- and 3-h acc updated every 5-min
6- to 72-h acc (updated hourly)
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Q2 PerformanceQuality Control and Adaptive Z-R
Q2 (research)
Stage II (operational)
Stage IV (operational)
24-h 12Z 4/25/07
24-h 12Z 4/25/07
24-h 12Z 4/25/07
Radar, satellite, and gauge Human intervention
Radar-only, automated
Radar model, automated
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Q2 PerformanceSeamless Mosaic
Stage IV
Q2
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Correction for Non-Uniform Vertical Profiles of
Reflectivity
before after
Bias 2.21 0.98
RMSE(mm) 5.15 1.09
bright band
KCLE 1-h rainfall ending10Z 11/15/08
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Future Directions

• Fully integration of dual-pol radar QPE
  techniques

• Evaluations (in collaboration with NWS/OHD,
  National Climate Data Center, University of
  Oklahoma, and NCAR)

• Continued RD on
• Blockage mitigation
• Non-uniform vertical profile of reflectivity
  correction
• Local gauge bias correction
• Multi-sensor (radar, model, gauge, satellite)
  blended QPE

• Continued collaboration with NOAA/HydroMet
  Testbed
• Integrate gap-filling radars
• Refine snow line delineation
• Continued collaboration with hydro modeling
  (Coastal Inland FLooding Observation and
  Warning Project -- CI-FLOW)

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Summary

• Q2 is a real-time system that produces national
  QPE products with high- spatial and temporal
  resolution.

• Q2 is a testbed that facilitates rapid
  science-to-operations transfer for
  hydro-meteorological applications.

• Q2 has been serving many users in government
  agencies, universities, and private sector.

• Q2 will continue RD for advanced multi-sensor
  QPE.

Questions?